A Deep Learning Approach to Detect COVID-19 Patients from Chest X-ray Images

Haque, Khandaker Foysal; Abdelgawad, Ahmed

doi:10.3390/ai1030027

Cited by 36 publications

(26 citation statements)

References 60 publications

(47 reference statements)

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“…Images of the chest were produced by Narin et al [7] , and ResNet50 identification of X-ray signals showed a 98% CO2 signal detection rate. Haque and Abdelgawad compared the CNN model for a COVID-19 positive patient [8] . This technique successfully finds and identifies patients who have Coronaviruses with almost no time or effort.…”

Section: Literature Surveymentioning

confidence: 99%

Feature extraction with capsule network for the COVID-19 disease prediction though X-ray images

Darji¹,

Nayak²,

Ganavdiya³

et al. 2022

Materials Today: Proceedings

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Past couple of years, the world is going through one of the biggest pandemic named COVID-19. In the mid of year 2019, it is a very difficult process to predict the COVID-19 just by viewing the images. Later on AI based technology has done a significant role in the prediction of COVID-19 through biomedical images such as CT scan, X ray etc. This study also implemented the deep learning model for the prediction of COVID-19 through X-ray images. The implemented model is termed as XR-CAPS which consist of two models such as U-Net model and the capsule network. The U Net model is used for performing the segmentation of the images and the capsule networks are applied for performing the feature extraction. The XR-CAPS model is applied on the X-ray images for the prediction of COVID-19 and the evaluation of the model is done by three parameters that are accuracy, sensitivity and specificity. The model is compared with other existing models like ResNet50, DenseNet121 and DenseCapsNet, this has achieved an accuracy of 93.2%, sensitivity of 94% and specificity of 97.1% which is better than other states of the art algorithms.

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Section: Literature Surveymentioning

confidence: 99%

Feature extraction with capsule network for the COVID-19 disease prediction though X-ray images

Darji¹,

Nayak²,

Ganavdiya³

et al. 2022

Materials Today: Proceedings

View full text Add to dashboard Cite

show abstract

“…Haque and Abdelgawad [ 24 ] have proposed a custom convolutional neural network model to detect COVID-19 patients using two different datasets containing normal and COVID-19 positive images. The proposed model achieved an accuracy of 98.3% using a second dataset.…”

Section: Related Literaturementioning

confidence: 99%

Artificial Neural Network-Based Deep Learning Model for COVID-19 Patient Detection Using X-Ray Chest Images

Masud

Alhumyani

Anand

et al. 2021

Journal of Healthcare Engineering

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The world is experiencing an unprecedented crisis due to the coronavirus disease (COVID-19) outbreak that has affected nearly 216 countries and territories across the globe. Since the pandemic outbreak, there is a growing interest in computational model-based diagnostic technologies to support the screening and diagnosis of COVID-19 cases using medical imaging such as chest X-ray (CXR) scans. It is discovered in initial studies that patients infected with COVID-19 show abnormalities in their CXR images that represent specific radiological patterns. Still, detection of these patterns is challenging and time-consuming even for skilled radiologists. In this study, we propose a novel convolutional neural network- (CNN-) based deep learning fusion framework using the transfer learning concept where parameters (weights) from different models are combined into a single model to extract features from images which are then fed to a custom classifier for prediction. We use gradient-weighted class activation mapping to visualize the infected areas of CXR images. Furthermore, we provide feature representation through visualization to gain a deeper understanding of the class separability of the studied models with respect to COVID-19 detection. Cross-validation studies are used to assess the performance of the proposed models using open-access datasets containing healthy and both COVID-19 and other pneumonia infected CXR images. Evaluation results show that the best performing fusion model can attain a classification accuracy of 95.49% with a high level of sensitivity and specificity.

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“…However, the paper considered a small dataset and did not specify the data source used. Haque and Abdelgawad [ 38 ] also provided binary classification using transfer learning, which is a technique to initialize the weights of a network based on a model pre-trained on a larger dataset and then fine-tuning it according to new and specific information [ 39 ]. Although the authors analyzed two distinct sets of images, only one database was used in each class, which may bias the created model.…”

Section: Overview Of Cnn and Covid-19 Diagnostic Modelsmentioning

confidence: 99%

Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases

et al. 2021

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As SARS-CoV-2 has spread quickly throughout the world, the scientific community has spent major efforts on better understanding the characteristics of the virus and possible means to prevent, diagnose, and treat COVID-19. A valid approach presented in the literature is to develop an image-based method to support COVID-19 diagnosis using convolutional neural networks (CNN). Because the availability of radiological data is rather limited due to the novelty of COVID-19, several methodologies consider reduced datasets, which may be inadequate, biasing the model. Here, we performed an analysis combining six different databases using chest X-ray images from open datasets to distinguish images of infected patients while differentiating COVID-19 and pneumonia from ‘no-findings’ images. In addition, the performance of models created from fewer databases, which may imperceptibly overestimate their results, is discussed. Two CNN-based architectures were created to process images of different sizes (512 × 512, 768 × 768, 1024 × 1024, and 1536 × 1536). Our best model achieved a balanced accuracy (BA) of 87.7% in predicting one of the three classes (‘no-findings’, ‘COVID-19’, and ‘pneumonia’) and a specific balanced precision of 97.0% for ‘COVID-19’ class. We also provided binary classification with a precision of 91.0% for detection of sick patients (i.e., with COVID-19 or pneumonia) and 98.4% for COVID-19 detection (i.e., differentiating from ‘no-findings’ or ‘pneumonia’). Indeed, despite we achieved an unrealistic 97.2% BA performance for one specific case, the proposed methodology of using multiple databases achieved better and less inflated results than from models with specific image datasets for training. Thus, this framework is promising for a low-cost, fast, and noninvasive means to support the diagnosis of COVID-19.

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A Deep Learning Approach to Detect COVID-19 Patients from Chest X-ray Images

Cited by 36 publications

References 60 publications

Feature extraction with capsule network for the COVID-19 disease prediction though X-ray images

Feature extraction with capsule network for the COVID-19 disease prediction though X-ray images

Artificial Neural Network-Based Deep Learning Model for COVID-19 Patient Detection Using X-Ray Chest Images

Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases

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